1. For Friday Read chapter 22 Program 4 due

2. Program 4 Any questions?

3. Learning mini-project Worth 2 homeworks Due Monday Foil6 is available in /home/mecalif/public/itk340/foil A manual and sample data files are there as well. Create a data file that will allow FOIL to learn rules for a sister/2 relation from background relations of parent/2, male/1, and female/1. You can look in the prolog folder of my 327 folder for sample data if you like. Electronically submit your data file—which should be named sister.d, and turn in a hard copy of the rules FOIL learns.

4. Strategies for Learning a Single Rule Top­Down (General to Specific): –Start with the most general (empty) rule. –Repeatedly add feature constraints that eliminate negatives while retaining positives. –Stop when only positives are covered. Bottom­Up (Specific to General): –Start with a most specific rule (complete description of a single instance). –Repeatedly eliminate feature constraints in order to cover more positive examples. –Stop when further generalization results in covering negatives.

5. FOIL Basic top­down sequential covering algorithm adapted for Prolog clauses. Background provided extensionally. Initialize clause for target predicate P to P(X 1,...X r ) :­. Possible specializations of a clause include adding all possible literals: –Q i (V 1,...V r ) –not(Q i (V 1,...V r )) –X i = X j –not(X i = X ) where X's are variables in the existing clause, at least one of V 1,...V r is an existing variable, others can be new. Allow recursive literals if not cause infinite regress.

6. Foil Input Data Consider example of finding a path in a directed acyclic graph. Intended Clause: path(X,Y) :­ edge(X,Y). path(X,Y) :­ edge(X,Z), path (Z,Y). Examples edge: {,,,,, } path: {,,,,,,,,, } Negative examples of the target predicate can be provided directly or indirectly produced using a closed world assumption. Every pair not in positive tuples for path.

7. Example Induction + : {,,,,,,,,, } - : {,,,,,,,,,,,,,,,, } Start with empty rule: path(X,Y) :­. Among others, consider adding literal edge(X,Y) (also consider edge(Y,X), edge(X,Z), edge(Z,X), path(Y,X), path(X,Z), path(Z,X), X=Y, and negations) 6 positive tuples and NO negative tuples covered. Create “base case” and remove covered examples: path(X,Y) :­ edge(X,Y).

8. + : {,,, } - : {,,,,,,,,,,,,,,,,, } Start with new empty rule: path(X,Y) :­. Consider literal edge(X,Z) (among others...) 4 remaining positives satisfy it but so do 10 of 20 negatives Current rule: path(x,y) :­ edge(X,Z). Consider literal path(Z,Y) (as well as edge(X,Y), edge(Y,Z), edge(X,Z), path(Z,X), etc....) No negatives covered, complete clause. path(X,Y) :­ edge(X,Z), path(Z,Y). New clause actually covers all remaining positive tuples of path, so definition is complete.

9. Picking the Best Literal Based on information gain (similar to ID3). |p|*(log 2 (|p| /(|p|+|n|)) - log 2 (|P| /(|P|+|N|))) P is number of positives before adding literal L N is number of negatives before adding literal L p is number of positives after adding literal L n is number of negatives after adding literal L Given n predicates of arity m there are O(n2 m ) possible literals to chose from, so branching factor can be quite large.

10. Other Approaches Golem CHILL Foidl Bufoidl

11. Domains Any kind of concept learning where background knowledge is useful. Natural Language Processing Planning Chemistry and biology –DNA –Protein structure

12. Natural Language Processing What’s the goal?

13. Communication Communication for the speaker: –Intention: Decided why, when, and what information should be transmitted. May require planning and reasoning about agents' goals and beliefs. –Generation: Translating the information to be communicated into a string of words. –Synthesis: Output of string in desired modality, e.g.text on a screen or speech.

14. Communication (cont.) Communication for the hearer: –Perception: Mapping input modality to a string of words, e.g. optical character recognition or speech recognition. –Analysis: Determining the information content of the string. Syntactic interpretation (parsing): Find correct parse tree showing the phrase structure Semantic interpretation: Extract (literal) meaning of the string in some representation, e.g. FOPC. Pragmatic interpretation: Consider effect of overall context on the meaning of the sentence –Incorporation: Decide whether or not to believe the content of the string and add it to the KB.

15. Ambiguity Natural language sentences are highly ambiguous and must be disambiguated. I saw the man on the hill with the telescope. I saw the Grand Canyon flying to LA. I saw a jet flying to LA. Time flies like an arrow. Horse flies like a sugar cube. Time runners like a coach. Time cars like a Porsche.

16. Syntax Syntax concerns the proper ordering of words and its effect on meaning. The dog bit the boy. The boy bit the dog. * Bit boy the dog the Colorless green ideas sleep furiously.

17. Semantics Semantics concerns of meaning of words, phrases, and sentences. Generally restricted to “literal meaning” –“plant” as a photosynthetic organism –“plant” as a manufacturing facility –“plant” as the act of sowing

18. Pragmatics Pragmatics concerns the overall commuinicative and social context and its effect on interpretation. –Can you pass the salt? –Passerby: Does your dog bite? Clouseau: No. Passerby: (pets dog) Chomp! I thought you said your dog didn't bite!! Clouseau:That, sir, is not my dog!

19. Modular Processing acoustic/ phonetic syntaxsemanticspragmatics Speech recognition Parsing Sound waves wordsParse trees literal meaning meaning

20. Examples Phonetics “grey twine” vs. “great wine” “youth in Asia” vs. “euthanasia” “yawanna” ­> “do you want to” Syntax I ate spaghetti with a fork. I ate spaghetti with meatballs.

21. More Examples Semantics I put the plant in the window. Ford put the plant in Mexico. The dog is in the pen. The ink is in the pen. Pragmatics The ham sandwich wants another beer. John thinks vanilla.